Federated Neuromorphic Learning (FNL) applies Spiking Neural Networks (SNNs) to enable energy-efficient collaborative learning on devices without centralizing data.However, integrating Differential Privacy (DP) introduces critical changes to the SNN firing dynamics, which propagate to server coordination strategies.This paper investigates DPinduced firing-rate distortions and their influence on global model convergence and generalization.Experimental ablation studies across privacy budgets and clipping bounds highlight firing distortions directly related to global accuracy degradation.Additionally, client selection instabilities related to DP noise degrade the model aggregation performance.The results reinforce that firing-rate-based FNL strategies are fragile under DP and require precise calibration to maintain the effectiveness of federated coordination.

Abstract Background: Ensuring the quality of user stories is vital to Agile Software Development. Rule-based tools like AQUSA, based on the Quality User Story (QUS) framework, offer reliable structural checks but struggle with context-sensitive or pragmatic issues. Large Language Models (LLMs) have emerged as potential alternatives, yet prior studies often rely on small datasets, older models, or lack direct comparison with rule-based baselines. Objective: This study aims to assess the effectiveness of modern LLMs relative to a rule-based tool (AQUSA) for detecting defects in user stories, considering both structural and contextual dimensions. Method: We conduct a large-scale comparative evaluation involving AQUSA and three GPT-family LLMs (GPT-5, GPT-5-mini, and GPT-4), using 182 user stories drawn from three industrial datasets. We apply both quantitative metrics (precision, recall, F1-score) and qualitative analysis of feedback clarity and defect relevance. Results: GPT-5-mini achieved the highest recall (0.81) and overall F1-score (0.62), while AQUSA attained the highest precision (0.61) with significantly fewer false positives. GPT-5 showed high hallucination rates and instability; GPT-4 was overly conservative, leading to under-detection of defects. Conclusion: Neither rule-based nor GPT-family LLM-based approaches suffice in isolation. Rule-based tools enforce structural rigor, while LLMs capture nuanced linguistic and pragmatic flaws. We advocate a hybrid “Dual-gate” strategy—using AQUSA for structural validation followed by lightweight LLMs for contextual refinement—to improve the reliability and scalability of user story quality assessment in agile environments.

Agile Software Development (ASD) has become the mainstream software development method of choice. Its core fundamentals are based on Teamwork factors and the higher value of individuals and their interactions over processes and tools. However, there is no common understanding regarding the factors that should be considered for defining an ASD Teamwork construct. Driven by this problem, we present a thematic network that synthesizes the information presented in the literature, and eases knowledge sharing by defining a terminology. The thematic network is the result of the following process: (i) studies definition to be used as data source through a literature review; (ii) data extraction from these studies; (iii) data translation into codes; (iv) codes translation into themes; (v) creation of higher-order themes model; and (vi) assessment of synthesis trustworthiness. The resulting thematic network comprises four higher-order themes: Cohesion, Orientation, Shared Leadership, and Autonomy. We also evaluate the applicability of the identified themes in ASD Teamwork constructs in the literature. We concluded that the constructed thematic network can be generalized to ASD, and used as basis by researchers who intend to explore ASD Teamwork. Further, practitioners can use our results to understand agile teams’ dynamics better and improve their efficiency.

Agile Software Development (ASD) has become the mainstream software development method of choice.Its core fundamentals are based on Teamwork factors and the higher value it gives to individuals and their interactions over processes and tools.Teamwork and human factors have been addressed as essential topics in the literature, and researchers have stated the importance of measuring it to increase the chances of success of ASD projects.However, there is no common understanding regarding the factors that should be considered for defining an ASD Teamwork construct.Driven by this problem, this paper presents a thematic network that defines the themes (i.e., factors) that should be considered when addressing ASD Teamwork.The ASD Teamwork thematic network is the result of a process that consisted of (i) defining the studies used as a data source through a literature review; (ii) extracting data from these studies; (iii) translating this data into codes; (iv) translating the codes into themes; (v) creating the model of higher-order themes; and, (vi) assessing the trustworthiness of the synthesis.The resulting thematic network comprises four higher-level themes: Cohesion, Orientation, Shared Leadership, and Autonomy.We believe that the constructed thematic network can be generalized to ASD and used as the basis by researchers who intend to explore ASD Teamwork.Further, practitioners can use our results to understand agile teams' dynamics better and improve their efficiency.

Using knowledge-based systems for helping agile teams to improve their performance is not a fact in the industry.In previous work, we have presented Kaizen, a knowledge-based Bayesian network for assisting Scrum teams in diagnosing their value stream in light of the predicted Customer Satisfaction and, consequently, improve their performance.This study assesses Kaizen's accuracy to predict Customer Satisfaction using realworld data.We adopted Kaizen for one software development company and collected data from 18 projects using an online questionnaire.We collected two types of data: inputs for Kaizen and the expected Customer satisfaction.We used the first type of collected data as inputs for Kaizen to calculate the predicted Customer satisfaction.Then, we assessed Kaizen's accuracy by comparing the predicted (i.e., calculated) and expected (i.e., collected) Customer satisfaction using face value and the average Brier score.Considering the face value, Kaizen predicted Customer Satisfaction correctly for 14 out of the 18 projects.The average Brier Score was 0.16.The model predicts, with satisfactory accuracy, the Customer Satisfaction and systemizes the process for Scrum teams to self-diagnose, enabling for causal analysis and supporting their continuous improvement.

The Internet-of-Things (IoT) scenarios commonly present security and privacy concerns, either due to the processing constraints of devices or the employment of external servers to process and store data, for instance, in cloud-based IoT applications. In this sense, to protect data and decrease user distrust in external entities, security technologies are of utmost importance. Trusted execution environments (TEEs), which process data in an isolated and protected region of memory, are among these technologies. We focus on a trusted architecture solution based on the use of TEEs and the application of authentication, authorization, and encryption mechanisms to protect data in IoT applications. We specified the trusted IoT architecture (TIoTA) using hierarchical colored Petri nets, and performed simulations and model checking of key security properties related to desired and prohibited behaviors, enabling model-based testing. This article enhances the state of the art by providing project artifacts (e.g., executable and parametric models) for correctly implementing the TIoTA and by presenting evidence that the usage of the architecture can improve security and privacy.

With the evolution of computer systems, the amount of sensitive data to be stored as well as the number of threats on these data grow up, making the data confidentiality increasingly important to computer users. Currently, with devices always connected to the Internet, the use of cloud data storage services has become practical and common, allowing quick access to such data wherever the user is. Such practicality brings with it a concern, precisely the confidentiality of the data which is delivered to third parties for storage. In the home environment, disk encryption tools have gained special attention from users, being used on personal computers and also having native options in some smartphone operating systems. The present work uses the data sealing, feature provided by the Intel Software Guard Extensions (Intel SGX) technology, for file encryption. A virtual file system is created in which applications can store their data, keeping the security guarantees provided by the Intel SGX technology, before send the data to a storage provider. This way, even if the storage provider is compromised, the data are safe. To validate the proposal, the Cryptomator software, which is a free client-side encryption tool for cloud files, was integrated with an Intel SGX application (enclave) for data sealing. The results demonstrate that the solution is feasible, in terms of performance and security, and can be expanded and refined for practical use and integration with cloud synchronization services.

The development of tools and smart devices has grown exponentially over the past few years, most due to the appearance of the Internet of Things (IoT).The large number of different connected devices highlighted challenges such as interoperability, scalability and reliability.In this scenario, for the development of new services and applications, it is necessary to use software platforms that ease the deployment and validation based on those challenges.With this, arises the need for middlewares, platforms that provide an environment for the development of such applications.In this way, in order to provide a simple, lightweight and dynamic approach, this article presents a study and development of an architecture that allows the communication, control and monitoring of IoT devices using an intuitive manner through an actor model.This is possible through the integration of the Calvin framework, the MQTT protocol and the OCF data model, providing an interoperable, reliable, dynamic and remote communication.A smart home environment was used for validation showing the relevance of the proposal.

One challenge in constructing a Bayesian network (BN) is defining the node probability tables (NPTs), which can be learned from data or elicited from domain experts.In practice, for large-scale BN it is common not to have enough data for learning and elicitation from experts is unfeasible.Previous work proposed a solution to this problem: the Ranked Nodes Method (RNM).However, this solution needs to be applied by a RNM expert who, through the elicitation of expert judgement, identifies the necessary parameters for the RNM algorithm to generate the NPTs.Hence, this paper presents a novel approach to define NPT using the RNM with no ranked nodes-specific knowledge.The solution is named Simulated Bayesian Network Expert (SBNE).It consists of eliciting a subset of the NPT from the domain experts which is used as input to an algorithm that estimates the optimal parameters for the RNM to generate the NPTs.To validate our solution, we conducted an experiment with multiple domain experts and compared the results with other methods.Our solution outperformed the other methods (producing NPTs at least 12% more accurate) and is, therefore, a promising approach to apply RNM without relying on RNM experts.

Background: The focus of Agile software development (ASD) is different than plan-driven development, requiring new software process improvement (SPI) paradigms.Objective: To identify and synthesize the possible gaps of Agile improvement solutions (AIS) given their focus on people factors, report of successful adoption in industry projects and availability of tool support.Method: We applied a Systematic Literature Review of studies published up to (and including) 2017 through backward and forward snowballing given a start set.Results: In total, we evaluated 55 papers, of which 44 included AIS and the main findings are: 1) 26 consider teamwork factors; 2) 21 were applied on industry; 3) 10 out of these 21 presented evidence of increase in company performance; and 4) 19 of the solutions are for the purpose of adoption, 18 for assessment and 8 are maturity models.Conclusion: The main implication for this research is a need for more and better empirical studies documenting and evaluating AIS.For the industry, the review provides a map of current AIS approaches and can be used as a starting point to adopt agile SPI.

Agile software development has been increasingly used to satisfy the need to respond to fast moving market demand and gain market share.Scrum, which is a project management framework, dominates as the most popular agile method.In the literature, there are a number of solutions to customize and assess Scrum-based agile methods, but they are limited to focus only on process factors, assume a predefined set of practices or rely only on subjective evaluation.This paper presents a framework to build a Bayesian Network to assist on the assessment of Scrum-based software development methods.The BN models the main entities of the software development process and can be complemented with software practices and metrics.To evaluate the completeness of our solution, we performed simulations to check if the proposed framework diagnoses 14 known Scrum anti-patterns extracted from the literature.12 antipatterns were directly detected, 1 was indirectly detected by the BN and 1 was considered as invalid.We concluded that the proposed solution is complete to detect the major flaws of Scrum-based software development methods and can be used to assist on the configuration, adoption and continuous improvement of Scrum teams.

Abstract Scrumis a simple process to understand, but hard to adopt. Therefore, there is a need for resources to assist on its adoption. In this paper, we present the process followed to build aBayesian networkto assist on the assessment of the quality of the software process in the context ofScrumprojects. The model provides data to helpScrum Masterslead the improvement of business value delivery ofScrumteams. The process is divided into 2 phases. In the first phase, we built theBayesian networkbased on expert knowledge extracted from the literature and experts. We used a top‐down approach and reasoning to define the key metrics necessary to build the models and their relationships. In the second phase, we updated theBayesian networkbased on limitations of the first version. We validated theBayesian networkinferences with 10 simulated scenarios. Comparing both versions, for all scenarios, we improved the accuracy of the inferences. Therefore, we concluded that theBayesian networksadequately representScrumprojects from the viewpoint of theScrumMaster. Finally, the model built is in conformance with agile methods tailoring and can be adapted to anyScrumteam.

The reliability on Safety Instrumented Systems (SIS) is critical for the safe operation of many industrial applications. In particular, SIS play an important role in oil and gas processing plants. SIS are responsible not only for the continuous operation of the plant, it also keeps the plant in a safe state, avoiding damages to the environment and minimizing risks to employees. Therefore, the correct behavior of such systems is an important goal to achieve when building industrial plants. Verification and testing of SIS programs is a very hard task to accomplish. This happens mainly for two reasons. First, testing the real system is very expensive and sometimes it may take a huge amount of time, weeks or even months. Second, those systems deal with a huge number of variables. It is not always possible for a human tester to check if all of them are correct when performing tests. Providing an automatic and formal testing approach for such systems is an important contribution for the development of such systems. In this work we introduce a new method for generating test cases for SIS programs running on Programable Logic Controller (PLC). As we did on previous work, ISA 5.2 diagrams are used as specification of the systems, but now we are using a hardware-in-the-loop technique, and the target artifact is a software running on a PLC. To avoid the execution of redundant tests, we introduced a new test case generation algorithm that is based on equivalence classes. Finally, we discussed a study case in which our method is used to detect error, that were introduced on purpose, on a simple system.

Population aging indicates a high prevalence of chronic illnesses, such as Dementia. Dementia is a chronic and incurable disease that affects several areas of brain, including cognitive areas such as memory, attention, language, and problem solving. The effect of these symptoms causes disability and dependency and it is essential to have a continuous monitoring and assistance of the patients. The disease affects not only the people who have it, but also their caregivers and families, leading them to a physical and emotional overload. To overcome this challenge, it is necessary to reduce the need for physical presence of a caregiver, still providing a constant monitoring. In this work, we propose a framework to support the development of applications to monitor people with Dementia based on a pervasive computing infrastructure, using sensors and mobile devices. To validate the proposed framework, we developed a case study focused on dementia caused by Alzheimer's disease.

In this work, the problems and respective solutions for dealing with two of the most basic questions regarding handoff management are presented: to avoid total loss of connectivity; and to reduce overall time of the handoff procedure. The focus of this research is on handoff in WPANs for real-time applications. Therefore, Bluetooth stands out as the ideal technology for case study due to its particular alignment with the behavior and requirements of the WPAN’s network model. Bluetooth’s inquiry and paging operations may be calibrated for achieving fast discoveries and connections while scatternets may be used to avoid total loss of connectivity. However, the usage of scatternets and calibration of inquiry and paging unveil a series of challenging aspects when applied along with real-time applications. Such challenges are the priority of inquiry and paging data traffic over users’ applications and unpredictability of scatternet behavior. The key point to overwhelm these difficulties is to understand how they affect the real-time data transfers. In this article a set of discussions and experiments that demonstrate how inquiries, pagings and scatternets can aid achieving the goals of fast and uninterrupted handoffs, as well as the tenuous traps and tradeoffs behind such operations and configurations over real-time applications performance are presented.

Safety Instrumented Systems (SIS) are usually designed to prevent accidents, avoid undesirable situations and guarantee continuous operation of oil and gas production systems. An interruption in the operation can be caused by faults in sensors and/or actuators. Hence, SIS are usu ally integrated to the supervisory control system in order to use the information from sensors to prevent such unde sirable situations. In this scenario, it is important to be able to validate the SIS implementation against its spec ification in order to increase the reliability of the system. In this work a technique to improve the dependability of SIS is introduced. A method to obtain a timed automata from a ISA 5.2 specification is presented and applied to a case study provided by Petrobras (Brazilian oil company). Finally, an approach to perform automatic testing of the implementation using the generated model is discussed. The method introduced here is based on the use of the Up paal model checker and the Uppaal-TRON testing tool.